title: 3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.
abstract: Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. Electronic address: smujiang@gmail.com.
authors.forename: Jun
authors.initials: J
authors.lastname: Jiang
authors.forename: Yao
authors.initials: Y
authors.lastname: Wu
authors.forename: Meiyan
authors.initials: M
authors.lastname: Huang
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Brain tumor; Graph-cut; Multimodal; Segmentation